The Friday Five – 5 Ways Radiomics Can Help Fight Lung Cancer
November is Lung Cancer Awareness Month
Even among healthcare insiders, radiomics remains somewhat of an unknown. Radiomics is the emerging field of science that extracts high-dimensional data from standard medical images to offer a comprehensive quantification of a potentially cancerous lesion or tumor’s physical characteristics, according to HealthMyne (@healthmyne), a pioneer in applied radiomics. In short, think “precision imaging analytics.”
This week’s Friday Five identifies five benefits radiomics brings to the fight against lung cancer.
Seeing “beneath the line”
When radiologists typically review images of potentially cancerous lesions or tumors, their view is limited to only two dimensions – length and width (the long and the short). With radiomics, clinicians can see “below the line” by capturing more than 1,500 radiomic data points from digital images, then analyzing that quantitative imaging data to deliver a wealth of new information.
Expedite drug development
Radiomics can help accelerate drug development by enabling researchers to compare novel data from sequential images to determine how a lesion has changed, indicating whether the drug is effective, why it is effective, to what degree it is effective, and how long it takes to achieve that effect.
Better personalization of treatments
By combing the phenotypic data radiomics derives from images with genomic data, healthcare organizations can evaluate disease progression, monitor therapy response, and determine clinical outcomes – accelerating the development and delivery of the best possible treatments every time.
Identification of new biomarkers
Imaging biomarkers, extracted quantifiable imaging patterns, have proven to be both predictive and prognostic regarding clinical outcomes and treatment pathways. Once they are identified, biomarkers can be used to inform and manage patient trial recruitment and trial design.
Continuous improvement through deep learning
Radiomics starts by building an artificial-intelligence-enabled model based on thousands of healthy organ images. Then, an algorithm ingests data variations from that model that provides new insights into lesions, tumors, and their progress. Deep learning capabilities enable radiomics systems to improve themselves as more images are ingested and analyzed and more outcomes are confirmed.
Learn more about radiomics at HealthMyne’s website.